Let's Share VMs: Optimal Placement and Pricing across Base Stations in MEC Systems

TL;DR

This paper introduces a joint VM placement and pricing framework for MEC systems that enhances revenue by up to 50% through collaborative base station resource sharing, using novel algorithms and auction mechanisms.

Contribution

It proposes a new joint VM placement and pricing approach with algorithms and auctions ensuring truthfulness and incentive compatibility in MEC systems.

Findings

Collaborative VM sharing increases revenue by up to 50%.

The proposed algorithms are proven to be incentive compatible and revenue-guaranteed.

The framework effectively balances demand and supply across base stations.

Abstract

In mobile edge computing (MEC) systems, users offload computationally intensive tasks to edge servers at base stations. However, with unequal demand across the network, there might be excess demand at some locations and underutilized resources at other locations. To address such load-unbalanced problem in MEC systems, in this paper we propose virtual machines (VMs) sharing across base stations. Specifically, we consider the joint VM placement and pricing problem across base stations to match demand and supply and maximize revenue at the network level. To make this problem tractable, we decompose it into master and slave problems. For the placement master problem, we propose a Markov approximation algorithm MAP on the design of a continuous time Markov chain. As for the pricing slave problem, we propose OPA - an optimal VM pricing auction, where all users are truthful. Furthermore, given users' potential untruthful behaviors, we propose an incentive compatible auction iCAT along with a partitioning mechanism PUFF, for which we prove incentive compatibility and revenue guarantees. Finally, we combine MAP and OPA or PUFF to solve the original problem, and analyze the optimality gap. Simulation results show that collaborative base stations increases revenue by up to 50%.